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A Self-Adapting Approach for Forecast-Less Scheduling of Electrical Energy Storage Systems in a Liberalized Energy Market

Author

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  • Eleonora Riva Sanseverino

    (Department of Energy, Information engineering and Mathematical models (DEIM), University of Palermo, Viale delle Scienze, Edificio 9, Palermo 90128, Italy)

  • Maria Luisa Di Silvestre

    (Department of Energy, Information engineering and Mathematical models (DEIM), University of Palermo, Viale delle Scienze, Edificio 9, Palermo 90128, Italy)

  • Gaetano Zizzo

    (Department of Energy, Information engineering and Mathematical models (DEIM), University of Palermo, Viale delle Scienze, Edificio 9, Palermo 90128, Italy)

  • Roberto Gallea

    (Department of Chemical Engineering, Management, Computer Science, Mechanical Engineering (DICGIM), University of Palermo, Viale delle Scienze, Edificio 6, Palermo 90128, Italy)

  • Ninh Nguyen Quang

    (Department of Energy, Information engineering and Mathematical models (DEIM), University of Palermo, Viale delle Scienze, Edificio 9, Palermo 90128, Italy)

Abstract

In this paper, an original scheduling approach for optimal dispatch of electrical Energy Storage Systems (ESS) in modern distribution networks is proposed. The control system is based on fuzzy rules and does not use forecasts since it repairs the past history according to the real time data on the electrical energy cost, renewable energy production and load. When the system detects a worsening of performances, the fuzzy logic rule-based control system self-adapts its membership functions using an economic indicator. The common use, in the relevant literature, of forecasted values in such systems can lead to large errors and economic losses. Moreover the speed of calculation guaranteed by the fuzzy control system allows the execution of new calculations even with high frequency. After the Introduction section, where the state of the art on the topic is outlined, the problem formulation is presented and an interesting application of the considered approach to the control on a medium size battery with real world data is proposed.

Suggested Citation

  • Eleonora Riva Sanseverino & Maria Luisa Di Silvestre & Gaetano Zizzo & Roberto Gallea & Ninh Nguyen Quang, 2013. "A Self-Adapting Approach for Forecast-Less Scheduling of Electrical Energy Storage Systems in a Liberalized Energy Market," Energies, MDPI, vol. 6(11), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:11:p:5738-5759:d:30147
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    References listed on IDEAS

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    1. Sanseverino, Eleonora Riva & Di Silvestre, Maria Luisa & Ippolito, Mariano Giuseppe & De Paola, Alessandra & Lo Re, Giuseppe, 2011. "An execution, monitoring and replanning approach for optimal energy management in microgrids," Energy, Elsevier, vol. 36(5), pages 3429-3436.
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    3. Khalid, M. & Savkin, A.V., 2010. "A model predictive control approach to the problem of wind power smoothing with controlled battery storage," Renewable Energy, Elsevier, vol. 35(7), pages 1520-1526.
    4. L. Stauffer & Th. Liebling, 1997. "Rolling horizon scheduling in a rolling-mill," Annals of Operations Research, Springer, vol. 69(0), pages 323-349, January.
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    Cited by:

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    2. Gopinath Subramani & Vigna K. Ramachandaramurthy & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Josep M. Guerrero, 2017. "Grid-Tied Photovoltaic and Battery Storage Systems with Malaysian Electricity Tariff—A Review on Maximum Demand Shaving," Energies, MDPI, vol. 10(11), pages 1-17, November.
    3. Eleonora Riva Sanseverino & Maria Luisa Di Silvestre & Romina Badalamenti & Ninh Quang Nguyen & Josep Maria Guerrero & Lexuan Meng, 2015. "Optimal Power Flow in Islanded Microgrids Using a Simple Distributed Algorithm," Energies, MDPI, vol. 8(10), pages 1-22, October.
    4. Bennett, Christopher J. & Stewart, Rodney A. & Lu, Jun Wei, 2015. "Development of a three-phase battery energy storage scheduling and operation system for low voltage distribution networks," Applied Energy, Elsevier, vol. 146(C), pages 122-134.
    5. Ippolito, M.G. & Di Silvestre, M.L. & Riva Sanseverino, E. & Zizzo, G. & Graditi, G., 2014. "Multi-objective optimized management of electrical energy storage systems in an islanded network with renewable energy sources under different design scenarios," Energy, Elsevier, vol. 64(C), pages 648-662.
    6. Honglu Zhu & Weiwei Lian & Lingxing Lu & Songyuan Dai & Yang Hu, 2017. "An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window," Energies, MDPI, vol. 10(10), pages 1-18, October.
    7. Yu-Shan Cheng & Yi-Hua Liu & Holger C. Hesse & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2018. "A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community," Energies, MDPI, vol. 11(2), pages 1-18, February.
    8. Graditi, G. & Ippolito, M.G. & Telaretti, E. & Zizzo, G., 2016. "Technical and economical assessment of distributed electrochemical storages for load shifting applications: An Italian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 515-523.
    9. Giovanni Artale & Giuseppe Caravello & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Salvatore Guaiana & Ninh Nguyen Quang & Marco Palmeri & Nicola Panzavecchia & Giovanni Tinè, 2020. "A Virtual Tool for Load Flow Analysis in a Micro-Grid," Energies, MDPI, vol. 13(12), pages 1-26, June.
    10. Giovanni Artale & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Riccardo Fiorelli & Salvatore Guaiana & Nicola Panzavecchia & Giovanni Tinè, 2019. "A New Coupling Solution for G3-PLC Employment in MV Smart Grids," Energies, MDPI, vol. 12(13), pages 1-23, June.
    11. Kyriakopoulos, Grigorios L. & Arabatzis, Garyfallos, 2016. "Electrical energy storage systems in electricity generation: Energy policies, innovative technologies, and regulatory regimes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1044-1067.

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